A Bayesian latent class analysis for whole-genome association analyses: an illustration using the GAW15 simulated rheumatoid arthritis dense scan data

Abstract

Although rheumatoid arthritis, a chronic and inflammatory disease affecting numerous adults, has a complex genetic component involving the human leukocyte antigen region, additional genomic regions most likely affects susceptibility. Whole-genome scans may assist in identifying these additional candidate regions, but a large number of false-positives are likely to occur using traditional statistical methods. Therefore, novel statistical approaches are needed. Here, we used a single replicate from the Genetic Analysis Workshop 15 simulated data to assess for marker-disease associations in 1500 rheumatoid arthritis cases and 2000 controls on chromosome 6. The statistical methods included a maximum-likelihood estimation approach and a novel Bayesian latent class analysis. The Bayesian analysis "borrows strength" from multiple loci to estimate association parameters and can incorporate differences across loci in the prior probability of association. Because of this, we hypothesized that the Bayesian analysis might be better able to detect true associations while minimizing false positives. The Bayesian posterior means for the log alleleic odds ratios were less variable than the maximum likelihood estimates, but the posterior probabilities were not as good as the simple p-values in distinguishing a signal from a non-signal. Overall, Bayesian latent class analyses provided no obvious improvement over maximum-likelihood estimation. However, our results may not be able to be generalized due to the large effect simulated in the human leukocyte antigen-DR locus.